Leveraging LLM-Based AI Agents for Boosting Vehicle Testing Process

<div class="section abstract"><div class="htmlview paragraph">The validation process in research and development involves several complex stages, including test requests, planning, execution, and the analysis and evaluation of results. In the automotive domain, compliance with regulatory standards, such as those required for Euro 7 homologation, adds an additional layer of complexity. Implementing these regulations into operational validation workflows and ensuring their seamless integration with supporting tools remains a significant challenge.</div><div class="htmlview paragraph">Recent advancements in Large Language Models (LLMs) have introduced innovative use cases across various domains. In particular, AI agents powered by LLMs demonstrate immense potential by autonomously performing complex tasks while utilizing user-defined tools. This capability extends far beyond traditional applications like knowledge management or text generation typically associated with LLMs.</div><div class="htmlview paragraph">In this paper, we explore how a modern AI agent can be developed and integrated into existing IT tools for test management to optimize the validation process. We present a feasibility study focused on the implementation of Euro 7 homologation requirements for braking systems. This includes functionalities such as generating test plans based on the Euro 7 regulation, performing software-based verification of constraints, and analyzing measurement data.</div><div class="htmlview paragraph">The proposed concepts are not limited to validation processes. The proposed AI agent concept can be seamlessly applied to other domains and integrated into additional products, leading to cost reductions and enhanced efficiency.</div></div>